Molecular profiling of small-molecules offers invaluable insights into the function of compounds and allows for hypothesis generation about small molecule direct targets and secondary effects. However, current profiling methods are either limited in the number of measurable parameters or throughput. Here, we developed a multiplexed, unbiased framework that, by linking genetic to drug-induced changes in nearly a thousand metabolites, allows for high-throughput functional annotation of compound libraries in Escherichia coli. First, we generated a reference map of metabolic changes from (CRISPR) interference with 352 genes in all major essential biological processes. Next, based on the comparison of genetic with 1342 drug-induced metabolic changes we made de novo predictions of compound functionality and revealed antibacterials with unconventional Modes of Action. We show that our framework, combining dynamic gene silencing with metabolomics, can be adapted as a general strategy for comprehensive high-throughput analysis of compound functionality, from bacteria to human cell lines.
In the version of this article initially published, an incorrect file for Supplementary Dataset 3 was inadvertently uploaded. Supplementary Dataset 3 has been updated with a file containing the correct sequencing results and enrichment analysis, which now correctly correlates with the screening summary file provided in Supplemental Dataset 5. We very much appreciate one of the readers of this article for pointing out this error. The changes have been made to the online version of the article.
Regulation of microtubule (MT) dynamics is key for mitotic spindle assembly and faithful chromosome segregation. Here we show that polyglutamylation, a still understudied post-translational modification of spindle MTs, is essential to define their dynamics within the range required for error-free chromosome segregation. We identify TTLL11 as an enzyme driving MT polyglutamylation in mitosis and show that reducing TTLL11 levels in human cells or zebrafish embryos compromises chromosome segregation fidelity and impairs early embryonic development. Our data reveal a mechanism to ensure genome stability in normal cells that is compromised in cancer cells that systematically downregulate TTLL11. Our data suggest a direct link between MT dynamics regulation, MT polyglutamylation and two salient features of tumour cells, aneuploidy and chromosome instability (CIN).
Background Independent Component Analysis (ICA) allows the dissection of omic datasets into modules that help to interpret global molecular signatures. The inherent randomness of this algorithm can be overcome by clustering many iterations of ICA together to obtain robust components. Existing algorithms for robust ICA are dependent on the choice of clustering method and on computing a potentially biased and large Pearson distance matrix. Results We present robustica, a Python-based package to compute robust independent components with a fully customizable clustering algorithm and distance metric. Here, we exploited its customizability to revisit and optimize robust ICA systematically. Of the 6 popular clustering algorithms considered, DBSCAN performed the best at clustering independent components across ICA iterations. To enable using Euclidean distances, we created a subroutine that infers and corrects the components’ signs across ICA iterations. Our subroutine increased the resolution, robustness, and computational efficiency of the algorithm. Finally, we show the applicability of robustica by dissecting over 500 tumor samples from low-grade glioma (LGG) patients, where we define two new gene expression modules with key modulators of tumor progression upon IDH1 and TP53 mutagenesis. Conclusion robustica brings precise, efficient, and customizable robust ICA into the Python toolbox. Through its customizability, we explored how different clustering algorithms and distance metrics can further optimize robust ICA. Then, we showcased how robustica can be used to discover gene modules associated with combinations of features of biological interest. Taken together, given the broad applicability of ICA for omic data analysis, we envision robustica will facilitate the seamless computation and integration of robust independent components in large pipelines.
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